Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
PhragmnÕs Voting Methods and Justified Representation
Authors: Markus Brill, Rupert Freeman, Svante Janson, Martin Lackner
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study Phragm en s methods from an axiomatic point of view, focussing on justified representation and related properties that have recently been introduced by Aziz et al. (2015a) and S anchez-Fern andez et al. (2017). We show that the sequential variant satisfies proportional justified representation, making it the first known polynomial-time computable method with this property. Moreover, we show that the optimization variants satisfy perfect representation. We also analyze the computational complexity of Phragm en s methods and provide mixed-integer programming based algorithms for computing them. |
| Researcher Affiliation | Academia | Markus Brill University of Oxford EMAIL Rupert Freeman Duke University EMAIL Svante Janson Uppsala University EMAIL Martin Lackner University of Oxford EMAIL |
| Pseudocode | Yes | Algorithm 1: Computing max-Phragm en |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical evaluation. The examples provided are illustrative, not empirical data. |
| Dataset Splits | No | The paper does not involve empirical experiments with datasets, and therefore no specific dataset split information (train/validation/test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper mentions 'mixed-integer linear and quadratic programming' as the basis for algorithms but does not specify any software names with version numbers for dependencies. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and properties but does not detail an experimental setup, including specific hyperparameters or system-level training settings. |